{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Care plans\n",
    "\n",
    "Care plans are used by staff to keep track of both current and planned procedures for the patient. Care plans are documented in five tables: carePlanCareProvider, carePlanEOL, carePlanGeneral, carePlanGoal, and carePlanInfectiousDisease."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alistairewj/.local/lib/python3.5/site-packages/psycopg2/__init__.py:144: UserWarning: The psycopg2 wheel package will be renamed from release 2.8; in order to keep installing from binary please use \"pip install psycopg2-binary\" instead. For details see: <http://initd.org/psycopg/docs/install.html#binary-install-from-pypi>.\n",
      "  \"\"\")\n"
     ]
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import getpass\n",
    "import pdvega\n",
    "\n",
    "# for configuring connection \n",
    "from configobj import ConfigObj\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Database: eicu\n",
      "Username: alistairewj\n"
     ]
    }
   ],
   "source": [
    "# Create a database connection using settings from config file\n",
    "config='../db/config.ini'\n",
    "\n",
    "# connection info\n",
    "conn_info = dict()\n",
    "if os.path.isfile(config):\n",
    "    config = ConfigObj(config)\n",
    "    conn_info[\"sqluser\"] = config['username']\n",
    "    conn_info[\"sqlpass\"] = config['password']\n",
    "    conn_info[\"sqlhost\"] = config['host']\n",
    "    conn_info[\"sqlport\"] = config['port']\n",
    "    conn_info[\"dbname\"] = config['dbname']\n",
    "    conn_info[\"schema_name\"] = config['schema_name']\n",
    "else:\n",
    "    conn_info[\"sqluser\"] = 'postgres'\n",
    "    conn_info[\"sqlpass\"] = ''\n",
    "    conn_info[\"sqlhost\"] = 'localhost'\n",
    "    conn_info[\"sqlport\"] = 5432\n",
    "    conn_info[\"dbname\"] = 'eicu'\n",
    "    conn_info[\"schema_name\"] = 'public,eicu_crd'\n",
    "    \n",
    "# Connect to the eICU database\n",
    "print('Database: {}'.format(conn_info['dbname']))\n",
    "print('Username: {}'.format(conn_info[\"sqluser\"]))\n",
    "if conn_info[\"sqlpass\"] == '':\n",
    "    # try connecting without password, i.e. peer or OS authentication\n",
    "    try:\n",
    "        if (conn_info[\"sqlhost\"] == 'localhost') & (conn_info[\"sqlport\"]=='5432'):\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   user=conn_info[\"sqluser\"])            \n",
    "        else:\n",
    "            con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                                   host=conn_info[\"sqlhost\"],\n",
    "                                   port=conn_info[\"sqlport\"],\n",
    "                                   user=conn_info[\"sqluser\"])\n",
    "    except:\n",
    "        conn_info[\"sqlpass\"] = getpass.getpass('Password: ')\n",
    "\n",
    "        con = psycopg2.connect(dbname=conn_info[\"dbname\"],\n",
    "                               host=conn_info[\"sqlhost\"],\n",
    "                               port=conn_info[\"sqlport\"],\n",
    "                               user=conn_info[\"sqluser\"],\n",
    "                               password=conn_info[\"sqlpass\"])\n",
    "query_schema = 'set search_path to ' + conn_info['schema_name'] + ';'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summarize data available in each table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Examine data for one patient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hospitals with data available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hospitalid</th>\n",
       "      <th>number_of_patients</th>\n",
       "      <th>n_cpc</th>\n",
       "      <th>n_cpeol</th>\n",
       "      <th>n_cpge</th>\n",
       "      <th>n_cpgo</th>\n",
       "      <th>n_cpid</th>\n",
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      "text/plain": [
       "     hospitalid  number_of_patients      n_cpc   n_cpeol     n_cpge  \\\n",
       "11           73                7059  86.669500  0.000000  99.065023   \n",
       "54          167                6092  52.954695  0.082075  99.753775   \n",
       "106         264                5237  96.429253  0.038190  98.644262   \n",
       "184         420                4679  99.251977  3.483650  96.281257   \n",
       "58          176                4328  52.356747  0.023105  99.745841   \n",
       "134         338                4277  97.685293  0.023381  99.415478   \n",
       "90          243                4243  97.784586  0.400660  97.407495   \n",
       "71          199                4240  91.910377  0.259434  95.353774   \n",
       "206         458                3701  98.324777  0.000000  99.000270   \n",
       "200         443                3656  92.970460  0.000000  99.234136   \n",
       "\n",
       "        n_cpgo    n_cpid  \n",
       "11    0.000000  0.000000  \n",
       "54    0.000000  0.016415  \n",
       "106   0.000000  0.000000  \n",
       "184  91.194700  1.816627  \n",
       "58    0.000000  0.000000  \n",
       "134   0.140285  0.023381  \n",
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       "71    0.306604  0.141509  \n",
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      ]
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     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "with cpc as ( select distinct patientunitstayid from careplancareprovider )\n",
    ", cpeol as ( select distinct patientunitstayid from careplaneol )\n",
    ", cpge as ( select distinct patientunitstayid from careplangeneral )\n",
    ", cpgo as ( select distinct patientunitstayid from careplangoal )\n",
    ", cpid as ( select distinct patientunitstayid from careplaninfectiousdisease )\n",
    "select \n",
    "  pt.hospitalid\n",
    "  , count(distinct pt.patientunitstayid) as number_of_patients\n",
    "  , count(distinct cpc.patientunitstayid) as n_cpc\n",
    "  , count(distinct cpeol.patientunitstayid) as n_cpeol\n",
    "  , count(distinct cpge.patientunitstayid) as n_cpge\n",
    "  , count(distinct cpgo.patientunitstayid) as n_cpgo\n",
    "  , count(distinct cpid.patientunitstayid) as n_cpid\n",
    "from patient pt\n",
    "left join cpc\n",
    "  on pt.patientunitstayid = cpc.patientunitstayid\n",
    "left join cpeol\n",
    "  on pt.patientunitstayid = cpeol.patientunitstayid\n",
    "left join cpge\n",
    "  on pt.patientunitstayid = cpge.patientunitstayid\n",
    "left join cpgo\n",
    "  on pt.patientunitstayid = cpgo.patientunitstayid\n",
    "left join cpid\n",
    "  on pt.patientunitstayid = cpid.patientunitstayid\n",
    "group by pt.hospitalid\n",
    "\"\"\".format()\n",
    "\n",
    "df = pd.read_sql_query(query, con)\n",
    "# convert to percent\n",
    "cols = ['n_cpc', 'n_cpeol', 'n_cpge', 'n_cpgo', 'n_cpid']\n",
    "for c in cols:\n",
    "    df[c] = df[c].astype(float) / df['number_of_patients'] * 100.0\n",
    "    \n",
    "df.sort_values('number_of_patients', ascending=False, inplace=True)\n",
    "df.head(n=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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\"quantitative\", \"bin\": {\"maxbins\": 10}, \"field\": \"Percent of patients with data\"}, \"color\": {\"type\": \"nominal\", \"field\": \"Number of hospitals\"}}, \"height\": 300, \"width\": 450};\n",
       "var selector = \"#1a990d24-8602-4cf3-8191-479616d8aeb8\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega3/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega3": "#1a990d24-8602-4cf3-8191-479616d8aeb8"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega3": "#1a990d24-8602-4cf3-8191-479616d8aeb8"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# rename columns within the call for clarity in the figure\n",
    "column_label = {'n_cpc': 'Care provider',\n",
    "               'n_cpeol': 'EOL',\n",
    "                'n_cpge': 'General',\n",
    "                'n_cpgo': 'Goal',\n",
    "                'n_cpid': 'Infectious disease'}\n",
    "\n",
    "df[cols].rename(columns=column_label).vgplot.hist(bins=10, stacked=True,\n",
    "                                    var_name='Number of hospitals',\n",
    "                                    value_name='Percent of patients with data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the majority of hospitals do not have data for the goal, infectious disease, and EOL tables. Conversely, the care provider and general tables have good coverage."
   ]
  }
 ],
 "metadata": {
  "front-matter": {
   "date": "2015-09-01",
   "linktitle": "admissiondrug",
   "title": "admissiondrug"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
